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TAP: a targeted clinical genomics pipeline for detecting transcript variants using RNA-seq data

Overview of attention for article published in BMC Medical Genomics, September 2018
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Title
TAP: a targeted clinical genomics pipeline for detecting transcript variants using RNA-seq data
Published in
BMC Medical Genomics, September 2018
DOI 10.1186/s12920-018-0402-6
Pubmed ID
Authors

Readman Chiu, Ka Ming Nip, Justin Chu, Inanc Birol

Abstract

RNA-seq is a powerful and cost-effective technology for molecular diagnostics of cancer and other diseases, and it can reach its full potential when coupled with validated clinical-grade informatics tools. Despite recent advances in long-read sequencing, transcriptome assembly of short reads remains a useful and cost-effective methodology for unveiling transcript-level rearrangements and novel isoforms. One of the major concerns for adopting the proven de novo assembly approach for RNA-seq data in clinical settings has been the analysis turnaround time. To address this concern, we have developed a targeted approach to expedite assembly and analysis of RNA-seq data. Here we present our Targeted Assembly Pipeline (TAP), which consists of four stages: 1) alignment-free gene-level classification of RNA-seq reads using BioBloomTools, 2) de novo assembly of individual targets using Trans-ABySS, 3) alignment of assembled contigs to the reference genome and transcriptome with GMAP and BWA and 4) structural and splicing variant detection using PAVFinder. We show that PAVFinder is a robust gene fusion detection tool when compared to established methods such as Tophat-Fusion and deFuse on simulated data of 448 events. Using the Leucegene acute myeloid leukemia (AML) RNA-seq data and a set of 580 COSMIC target genes, TAP identified a wide range of hallmark molecular anomalies including gene fusions, tandem duplications, insertions and deletions in agreement with published literature results. Moreover, also in this dataset, TAP captured AML-specific splicing variants such as skipped exons and novel splice sites reported in studies elsewhere. Running time of TAP on 100-150 million read pairs and a 580-gene set is one to 2 hours on a 48-core machine. We demonstrated that TAP is a fast and robust RNA-seq variant detection pipeline that is potentially amenable to clinical applications. TAP is available at http://www.bcgsc.ca/platform/bioinfo/software/pavfinder.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 22%
Researcher 5 14%
Student > Master 4 11%
Professor 2 6%
Other 2 6%
Other 4 11%
Unknown 11 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 31%
Agricultural and Biological Sciences 7 19%
Medicine and Dentistry 4 11%
Immunology and Microbiology 1 3%
Neuroscience 1 3%
Other 0 0%
Unknown 12 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2018.
All research outputs
#18,649,291
of 23,103,436 outputs
Outputs from BMC Medical Genomics
#871
of 1,238 outputs
Outputs of similar age
#258,917
of 337,287 outputs
Outputs of similar age from BMC Medical Genomics
#14
of 22 outputs
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So far Altmetric has tracked 1,238 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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